An Innovative Application from the DARPA Knowledge Bases Programs: Rapid Development of a Course-of-Action Critiquer

Gheorghe Tecuci, Mihai Boicu, Mike Bowman, Dorin Marcu

Abstract

This article presents a learning agent shell and methodology for building knowledge bases and agents and their innovative application to the development of a critiquing agent for military courses of action, a challenge problem set by the Defense Advanced Research Projects Agency's High-Performance Knowledge Bases Program. The learning agent shell includes a general problem-solving engine and a general learning engine for a generic knowledge base structured into two main components: (1) an ontology that defines the concepts from an application domain and (2) a set of task-reduction rules expressed with these concepts. The development of the critiquing agent was done by importing ontological knowledge from cyc and teaching the agent how an expert performs the critiquing task. The learning agent shell, the methodology, and the developed critiquer were evaluated in several intensive studies, demonstrating good results.